{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python371jvsc74a57bd0ceed3ede7d2ae4746b1bde0ed48f83d28ba93d0b68e140a25bb2fbb7cbabeb22",
   "display_name": "Python 3.7.1 64-bit ('Python3_7_2')"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import numpy as np \n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "train_x = np.random.randn(50)\n",
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 1.04273457, -1.20653872, -1.15822152,  0.09317578, -2.03328488,\n",
       "        1.37923362, -0.88418025,  0.87515419,  0.92182501,  0.86563837,\n",
       "       -0.33404037,  1.48340089,  1.00622893,  0.30159037,  0.72172509,\n",
       "        0.01332197, -0.45373574, -0.28785836,  0.40029257, -1.1695448 ,\n",
       "        1.44685749, -0.52011203,  2.19073223,  0.05888204,  0.12111199,\n",
       "        0.10508122,  1.40072157,  0.4882268 , -0.94678041,  0.5992846 ,\n",
       "        0.07000267, -0.53177797, -1.49418874,  0.7644935 , -0.09870421,\n",
       "       -0.20302486,  0.84968366, -0.73603717,  0.49175046,  0.36454328,\n",
       "       -0.46590281,  0.91362875, -0.26194016, -1.26922493,  1.90270199,\n",
       "        0.77213368,  2.84908647, -0.77182087,  1.59501732,  1.35174095])"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trai_y"
   ]
  }
 ]
}